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Homomorphic Encryption, a magic wand in the field of cryptography that enables the user to perform secure computation over encrypted data, without decrypting the data. It allows cloud service providers to have access to encrypted data instead of original data, which also facilitates secure computation over encrypted data along with the secure transmission of it. NTRU, open-source public-key cryptography, based on lattices, is one of the standards of Homomorphic Encryption, can be used for secure outsourcing, the transmission of data, and performing computation on encrypted data. NTRU scheme is not vulnerable to quantum computer-based attacks, unlike existing traditional cryptographic techniques. In this paper, NTRU, the homomorphic encryption scheme is reviewed. The different variants of NTRU are also explainedthrough a detailed literature studybased on ring structure.The analysis also has been done that may help researchers to have a clear understanding of NTRU and its different variants and to distinguish them easily. The availability of different types of fields, rings based on different structures, different algebra, etc. for implementation of NTRU opens a new way for researchers to explore them to achieve a more efficient NTRU scheme for real-life applications. [ABSTRACT FROM AUTHOR]

As there is a continuous growth of big data, the researchers are showing interest in the applications of cloud computing concerning privacy, and security. On the other hand, many researchers and the experts of cybersecurity have commenced on a quest for improving the data encryption methods. Clients or data owners are more interested to outsource their data on commercial public clouds for more flexibility, security, confidentiality and to reduce economic investments. Under this circumstance, homomorphic encryption appears as a probable solution, in which the information of the client is encrypted on the cloud in such a process that it can allow computation on data without decryption. The main intent of this paper is to present the systematic review of research papers published in the pitch of integer-based fully homomorphic encryption. The performance measures and contributions are also analyzed. Furthermore, different platforms used for implementation, and the diverse platforms used for performing the FHE are also studied. Finally, it highlights the challenges and gaps to be addressed in modeling and learning about competent, effectual, and vigorous FHE for the cloud sector and pays attention to directions for better future research. [ABSTRACT FROM AUTHOR]

The main objective of this work is to provide a hazard free environment for power generation in thermal power plants. Estimation of Sulphur Dioxide (SO2) emissions from flame images in thermal and gas turbine power plants is of great importance in the domain of image processing. The primary objective in detection, recognition and understanding of combustion conditions offers a feed forward control action for minimizing flue gas emissions. In this work, soft sensors using feed forward neural network trained with Back Propagation Algorithm (BPA) and Particle Swarm Optimization (PSO) are used for flame image classification. The solution includes the Internet of Things (IoT) where the intelligent sensors are connected to the embedded computing system to monitor the fluctuation of parameters relating to the flame colour. The first step is to define a feature vector for each flame image including 8 feature elements, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid and Linear Binary pattern (LBP) respectively. The quality of the captured images is enhanced using curvelet transform. The concept of object (flame feature) recognition and classification (BPA and PSO) of the flame image is carried out to measure the SO2 emissions from the flame colour. A PC based online setup for flame monitoring to detect SO2 emissions in thermal and gas turbine power plants at the furnace level is proposed here. The effectiveness of the system is inferred from the specificity and sensitivity of the soft sensor. [ABSTRACT FROM AUTHOR]